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A platform for research: civil engineering, architecture and urbanism
Structural health monitoring of ASCE benchmark building using machine learning algorithms
In this study, machine learning algorithms are utilized to build a vibration-based structural health monitoring (SHM) method for steel frame structures. Therefore, the ASCE benchmark steel building structure is modeled in the ANSYS environment and subjected to impact loading to collect acceleration time history data for both damaged and undamaged cases. Statistical parameters, such as standard deviation, median, kurtosis, root mean square, skewness, mean absolute deviation, Renyi’s entropy, and Shannon entropy, are extracted from the collected data. These parameters are utilized in the classification of undamaged and damaged cases using Naive Bayes, artificial neural network, and support vector machine. The effectiveness and the robustness of the algorithm are verified under unseen datasets, along with the localization and quantification of the damage.
Structural health monitoring of ASCE benchmark building using machine learning algorithms
In this study, machine learning algorithms are utilized to build a vibration-based structural health monitoring (SHM) method for steel frame structures. Therefore, the ASCE benchmark steel building structure is modeled in the ANSYS environment and subjected to impact loading to collect acceleration time history data for both damaged and undamaged cases. Statistical parameters, such as standard deviation, median, kurtosis, root mean square, skewness, mean absolute deviation, Renyi’s entropy, and Shannon entropy, are extracted from the collected data. These parameters are utilized in the classification of undamaged and damaged cases using Naive Bayes, artificial neural network, and support vector machine. The effectiveness and the robustness of the algorithm are verified under unseen datasets, along with the localization and quantification of the damage.
Structural health monitoring of ASCE benchmark building using machine learning algorithms
Asian J Civ Eng
Palsara, Chandesh (author) / Kumar, Vimal (author) / Pal, Joy (author) / Naresh, M. (author)
Asian Journal of Civil Engineering ; 25 ; 303-316
2024-01-01
14 pages
Article (Journal)
Electronic Resource
English
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